SOTAVerified

Continual Learning

Continual Learning (also known as Incremental Learning, Life-long Learning) is a concept to learn a model for a large number of tasks sequentially without forgetting knowledge obtained from the preceding tasks, where the data in the old tasks are not available anymore during training new ones.
If not mentioned, the benchmarks here are Task-CL, where task-id is provided on validation.

Source:
Continual Learning by Asymmetric Loss Approximation with Single-Side Overestimation
Three scenarios for continual learning
Lifelong Machine Learning
Continual lifelong learning with neural networks: A review

Papers

Showing 110 of 2644 papers

TitleStatusHype
PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online LearningCode0
Information-Theoretic Generalization Bounds of Replay-based Continual Learning0
RegCL: Continual Adaptation of Segment Anything Model via Model Merging0
A Neural Network Model of Complementary Learning Systems: Pattern Separation and Completion for Continual Learning0
Fast Last-Iterate Convergence of SGD in the Smooth Interpolation Regime0
Overcoming catastrophic forgetting in neural networks0
LifelongPR: Lifelong knowledge fusion for point cloud place recognition based on replay and prompt learningCode0
Continual Reinforcement Learning by Planning with Online World Models0
The Bayesian Approach to Continual Learning: An Overview0
Rethinking Query-based Transformer for Continual Image SegmentationCode1
Show:102550
← PrevPage 1 of 265Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTRF1 - macro0.89Unverified
2CATF1 - macro0.87Unverified
3HATF1 - macro0.86Unverified
4KANF1 - macro0.81Unverified
5B-CLF1 - macro0.77Unverified
6EWCF1 - macro0.66Unverified